Adaptive neural network fault-tolerant control for stochastic nonlinear systems based on reinforcement learning

Abstract

This paper presents a reinforcement learning-based adaptive fault-tolerant optimal control strategy for a class of stochastic nonlinear systems subject to dead-zone input, sensor faults, and actuator faults. Initially, a reinforcement learning algorithm with an identifier-critic-actor neural networks structure is introduced, and an innovative optimal control scheme featuring prescribed-time convergence is designed. To tackle the issue of unmeasurable states due to sensor faults, a fault-tolerant coordinate transformation technique is employed. By leveraging the universal approximation property of neural networks, the unknown nonlinear terms in the system, as well as the uncertainties arising from faults and dead-zone, are effectively estimated. Moreover, to optimize the utilization of communication resources, an event-triggered fault-tolerant optimal controller is designed. Theoretical analysis demonstrates that the proposed algorithm ensures that all signals in the closed-loop system remain bounded in probability and that the tracking error converges to a neighborhood of the origin within the prescribed time. Finally, simulation results further validate the effectiveness of the designed control strategy.